2016计算机视觉和机器学习微分几何研讨会 Differential Geometry in Computer Vision and Machine Learning Workshop

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Riemannian geometric computing has received a lot of recent interest in the computer vision community. In particular, Riemannian geometric principles can be applied to a variety of difficult computer vision problems including face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion to name a few. Besides their nice mathematical formulation, Riemannian computations based on the geometry of underlying manifolds are often faster and more stable than their classical counterparts. Over the past few years, the popularity of Riemannian algorithms has increased several-fold. Some of the mathematical entities that benefit from a geometric analysis include rotation matrices, medial representations, subspace comparisons, symmetric positive-definite matrices, function spaces, and many more.

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Main topics of interest

Shape Representations: Silhouettes, Surfaces, Skeletons, Humans, etc..
Information Geometry: Fisher-Rao and elastic metrics, Gromov-Wasserstein family, Earth-Mover’s distance, etc.
Dynamical Systems: Trajectories on manifolds, Rate-invariance, Identification and classification of systems.
Domain Transfer: Ideas and applications.
Image/Volume/Trajectory: Spatial and temporal registration & segmentation.
Manifold-Valued Features: Histograms, Covariance, Symmetric positive-definite matrices, Mixture model.
Big Data: Dimension-reduction using geometric tools.
Bayesian Inferences: Nonlinear domains, Computational solutions using differential geometry, Variational approaches.
Machine Learning Approaches on Nonlinear Feature Spaces: Kernel methods, Boosting, SVM-type classification, Detection and tracking algorithms.
Functional Data Analysis: Hilbert manifolds, Visualization.
Applications: Medical analysis, Biometrics, Biology, Environmetrics, Graphics, Activity recognition, Bioinformatics, Pattern recognition, etc.
Geometry of articulated bodies: Applications to robotics, biomechanics, and motor control.
Computational topology and applications.